Table 3.

Single-cell multi-omics sequencing for epigenomic profiling together with their specific applications, results and data sources

Method	Data source	Molecular layers	Objective and outcome(s)	Platform(s)
scCOOL-seq [45]	GSE78140	Distinct layers of epigenomic information from 24 single mouse embryonic stem cells.	To simultaneously measure all the different layers of epigenomic information from the same individual cell. Factors other than DNA methylation are determinants of the heterogeneity of the open versus closed chromatin states of the promoter regions of genes.	Illumina HiSeq 2500.
scM&T-seq [46]	GSE76483	DNA methylome and transcriptome from the same cell.	To understand the direct correlation of DNA methylation and gene expression within single cells. Methylation of non-CGI promoters was better anticorrelated with gene transcription, whereas gene body methylation of CGI promoter genes was better correlated with gene transcription.	Illumina Hiseq 2500
scNMT-seq [47]	GSE109262	Transcriptome, chromatin accessibility and DNA methylation within single cells.	To uncover complete information of connections and dependent relationships among epigenetic layers together with transcription. scNMT-seq was able to robustly profile gene expression, DNA methylation and chromatin accessibility within the same single cell.	Illumina NextSeq 500
scTrio-seq [48]	GSE65364	Genomic CNVs, DNA methylome and transcriptome of an individual mammalian cell.	To detect subpopulations of cancer cells and the relationships among the three different types of omics. Negative correlation between promoter methylation and RNA expression and positive correlation between gene body methylation and RNA expression were found in a single cell, and a strong positive correlation was found between the DNA copy number and gene expression within the affected genomic region.	Illumina HiSeq2000 or HiSeq 2500 Sequencer
SHARE-seq [49]	GSE140203	Chromatin accessibility and gene expression within 84,426 cells across four different cell lines and three tissue types.	To recover fine but biologically important differences by measuring chromatin accessibility and gene expression within the same cell. The cell cycle was more associated with changes in gene expression and less prominent in chromatin accessibility profiles.	NextSeq 550 and Illumina NovaSeq 6000
sci-ATAC-seq [50]	GSE118987	Accessible chromatin landscape and transcriptomics information at 899 high-quality cells in cultured hippocampal neurons.	To comprehensively map the accessible chromatin landscape and transcriptomics in fresh and frozen hippocampal tissue samples. Cell type assignments with substantial concordance between the highest represented cell types across platforms.	Illumina NextSeq 500, Illumina HiSeq 2000, Illumina MiSeq and Illumina HiSeq 2500.
scCAT-seq [51]	SRA (SRP167062) and CNGB (CNP0000213).	Accessible chromatin and gene expression within the same single cell from a total of 192 samples.	To study how transcription factors and epigenomic features induce transcriptional outcomes that influence cell fate determinations. scCAT-seq data could recapitulate major features obtained by separately performed bulk ATAC-seq and RNA-seq.	BGISEQ-500
SNARE-seq [52]	GSE126074	Simultaneous profiling of gene expression and chromatin accessibility in each of thousands of single nuclei.	To enable highly parallel profiling of chromatin accessibility and mRNA from individual nuclei. SNARE-seq could effectively separate cell types on the basis of both their chromatin signatures and transcriptomes, with a high level of concordance.	Illumina HiSeq 2500, Illumina HiSeq 4000
10 Genomics scRNA +scATAC	https://www.10xge nomics.com/products/single-cell-multi ome–atac–plus–ge ne–expression	Simultaneous profiling of the transcriptome (using 3’ gene expression) and epigenome (using ATAC-seq) from single cells.	To deepen understanding of how genes are expressed and regulated across different cell types.	10 Genomics